T-ROC Global's Retail360 Wins 2025 TWICE VIP Award for Innovation in Retail Technology
'Retail360 represents the future of retail execution - where cutting-edge AI meets real human insight to deliver smarter, faster, and more personalized experiences at every level of the store,' said Brett Beveridge, CEO and Founder of T-ROC Global. 'This award is a testament to the incredible innovation and hard work of our team, and to our unwavering mission to transform retail through technology that truly works for people.'
The TWICE VIP Awards celebrate and recognize the best features, design and value that new products are bringing to consumers. Voted upon exclusively by TWICE magazines readers and subscribers, the leading voice in the consumer electronics industry, this achievement highlights which companies are truly going above and beyond to deliver value to consumers.
Retail360 features six core AI-driven solutions: Optinomics, WorkLink, NextStop, VisionIQ, SmartPix, and Navigator. Retail360 manages every step of the retail execution journey, from market planning to post-visit analysis.
It uses generative AI, machine learning, image recognition, and real-time competitive data to deliver clarity, consistency, and control across thousands of retail locations. From optimizing territory coverage to providing visual proof of shelf compliance, the platform surfaces data transforming it into actionable insight.
To learn more about Retail360 and how T-ROC Global is revolutionizing the retail landscape, visit www.trocglobal.com. T-ROC Global can also be found on Facebook, X, Instagram, and LinkedIn.
About The Revenue Optimization Companies (T-ROC Global)
T-ROC Global is a retail branding and consulting partner that supports companies in navigating through today's retail shopping experience, redefining the power of people and technology. T-ROC Global offers a unique combination of people-based services, applications, technology management, mystery shopping programs, actionable market research and competitive insights that support the complex needs of assisted selling. T-ROC Global's expertise and next-generation technology is delivered by a team that's all in to drive sales, optimize performance and deliver measurable ROI for businesses every single day.
Media Contact: Tyler Sminkey, (786) 390-8510, [email protected]
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Fast Company
6 minutes ago
- Fast Company
The trouble with Agent, ChatGPT's new web-browsing AI
Hello again, and thanks for reading Fast Company 's Plugged In. When you think about it, training AI to use the web might be the single most impactful way to expand its power. So much of what we do today—from buying products of all kinds to managing every aspect of our personal data—we do online. If a piece of software could handle that work at least as well as a human, it could be a far more essential assistant than any existing AI tool. Web savvy is key to the tech industry's current yen to make AI more agentic —that is, capable of performing multistep processes on our behalf with some degree of autonomy. A flurry of recent news reflects this trend. On July 9, for example, Perplexity launched Comet, a web browser with a built-in AI agent, available mostly to users of the company's $200/month plan. A week later, OpenAI began rolling out a new ChatGPT agent called . . . Agent. Microsoft is adding a Copilot mode to its Edge browser that it says will soon be able to perform tasks such as making reservations; Opera is previewing Opera Neon, its own browser with built-in agentic AI. I've been playing with OpenAI's Agent, which showed up in my ChatGPT Plus account earlier this week. The company's blog post on the feature raises expectations by describing it as 'already a powerful tool for handling complex tasks.' So far, however, my experiences with it have not provided any moments of awe and wonder. Instead, I've been left wondering if the era of offloading all kinds of web work to an LLM is further off than I thought. Tech companies have already trained AI to do some astonishing things, such as achieve gold-medal-level performance in the International Mathematical Olympiad. But Agent often came off like a clueless internet newbie banging its head against a medium conspiring to foil it. In its own odd way, watching Agent at work is fascinating. When you give it a prompt—as with AI of all types, the more detail you provide, the better—it opens a web browser on a remote OpenAI computer. Then it displays the web pages it's accessing right inside your chat and explains every step it's taking in absurd detail, down to which buttons it chooses to click. It's like peering into the feature's brain, and underscores the infinite number of tiny, almost subconscious decisions we make when using the web. More often than not, though, Agent's responses to my requests weren't worth the wait. It took 13 minutes to rummage through Google Flights for San Francisco-New York flight options, and the list it gave me was missing the itinerary I probably would have chosen. When I asked it to compile a list of the necessary ingredients to bake authentic German lebkuchen, it combined ones from two different recipes without any apparent logic. I fed it the description for a job opening here at Fast Company and asked it to find candidates; it suggested some, but with out-of-date information on their current employers. After a certain point, I wondered whether the projects I was throwing Agent's way were poor tests of its talents. So I tried several tasks ChatGPT suggests when you initiate an Agent session. Many of them, it whiffed. Agent could not log into my Wall Street Journal account to prepare a report on the site's coverage of rare earth materials, or verify my phone number to schedule an Uber pickup. While adding banana cream pie ingredients to an Instacart order, it plugged in a random delivery address and didn't seem to offer any way for me to correct it. A summary of Axios 's recent articles on AI worked better, except it didn't include anything from the past two weeks. (Agent was often confused about the current date, informing me at various points that it was July 15 or July 16 when it was actually July 30.) 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Associated Press
7 minutes ago
- Associated Press
VideoProc Converter AI Kicks Off Summer Campaign with Smarter Compression and Exclusive Offers
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For more information, please visit Viola Nee Digiarty Software Inc. +86 28 8513 4884 email us here Visit us on social media: Facebook YouTube X Legal Disclaimer: EIN Presswire provides this news content 'as is' without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.
Forbes
7 minutes ago
- Forbes
Why The AI Boom Depends On Materials Engineering
Sundeep Bajikar, Corporate Vice President, Corporate Strategy and Marketing, Applied Materials. AI is on track to more than double datacenter electricity demands by 2030. If we want large-scale intelligence and a livable planet, every tera-operation of compute must consume far fewer joules. For decades, Moore's Law delivered predictable improvements in the performance and power consumption of the most advanced computer chips, but simply shrinking transistors can no longer deliver that leap. The next efficiency curve is being written in materials engineering, and it will advance only as fast as the industry learns to co-innovate across company boundaries. How Big Is The Energy Gap? Increasing complexity and the growing number of parameters are driving exponential growth in computational requirements for training new large language models (LLMs). Over the past four years, we've witnessed the compute requirement for training increase while the cost of training has increased even faster. 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If AI is viewed as a significantly more productive technology, then it should be enabled to consume as much or more electricity to raise global productivity. Beyond transforming the electricity grid, the industry playbook will also require massive advances in energy-efficient computing. The semiconductor industry delivered a tremendous improvement in energy efficiency over the last 15 years. Exponential growth in AI computing requirements means another significant improvement in energy-efficient performance will be needed over the next 15 years. A Materials Engineering Playbook As early as 2012, while I was an equity analyst on Wall Street, I had observed to investors that the cadence of classic 2D Moore's Law scaling was slowing and that materials engineering would become the dominant lever for energy efficiency (performance-per-watt). A decade later, the data confirm this conclusion. The performance-per-watt frontier has shifted from photolithography to the atomic layer. Our internal analysis estimates that combining architectural advances with materials breakthroughs can unlock efficiency gains as large as 2,500x, orders of magnitude beyond what the next geometric shrink of feature sizes would deliver. Capital spending trends echo that pivot. At the most advanced logic nodes with gate-all-around (GAA) transistors and backside power delivery, incremental wafer fab equipment (WFE) outlays now target materials engineering technologies that define critical dimensions and electrical properties of nanoscale devices. In emerging compute memory (DRAM) 4F2 architectures, an even bigger portion of the incremental spend shifts toward materials engineering, with a reduction in lithography-related spending. We've estimated that GAA can provide a 25-30% improvement in energy efficiency. New inflections in compute memory have immense potential to improve AI computing's energy efficiency. The message is clear: Energy efficiency is now a materials problem. Why No One Can Go It Alone During the heyday of Moore's Law, a handful of vertically integrated giants could manage the entire scaling roadmap. That era is over. In the past decade, nearly every hyperscaler and several automakers have announced their own AI accelerators, each tailored to unique workloads and built on specialized process flows. Differentiation has migrated toward materials, packaging and architecture, and none of those domains is the exclusive province of a single company. A new, high-velocity model of co-innovation is therefore emerging: 1. Modular Designing: Open chiplet standards allowing designers to mix and match best-in-class dies, logic from one vendor, I/O from another, memory-on-logic from a third, without re-laying out entire systems. 2. Shared R&D Or Pilot Lines: Foundries, equipment suppliers and power electronics firms pooling capital to accelerate innovation and commercialization of advanced node technologies. 3. Joint Materials Roadmapping: Cloud providers, chipmakers and tool vendors convening regularly to align on feature targets, high-value problems and environmental metrics, compressing concept-to-production cycles. These collaborations are less about marketing alliances and more about spanning fundamental physics gaps that no single company can bridge. They let companies trade know-how for speed. Conclusion AI sustainability hinges on energy efficiency per operation. That efficiency is increasingly unlocked by materials engineering, not by chasing ever-smaller geometries with lithography alone. Delivering the next decade of performance gains and staying within a planetary power budget will require companies to co-create new atoms, interfaces and 3D structures at unprecedented speed. Firms that embrace this collaborative, materials-first roadmap will gain ground on shrinking carbon footprints and capturing a durable competitive edge. Those that cling to a lone-wolf strategy may find themselves running out of joules and time. Forbes Business Development Council is an invitation-only community for sales and biz dev executives. Do I qualify?
